An improved method for segmentation of mammographic masses
Computer aided diagnosis (CADx) systems can support the radiologist in the complex task of discriminating benign and malignant mammographic lesions. Automatic segmentation of mammographic lesions in regions of interest (ROIs) is a core module of many CADx systems. Previously, we have proposed a novel method for segmentation of mammographic masses. The approach was based on the observation that the optical density of a mass is usually high near its core and decreases towards its boundary. In the work at hand, we improve this approach by integration of a pre-processing module for the correction of inhomogeneous background tissue and by improved selection of the optimal mass contour from a list of candidates based on a cost function. We evaluate the performance of the proposed approach using ten-fold cross-validation on a database of mass lesions and ground-truth segmentations. Furthermore, we compare the improved segmentation approach with the previously proposed approach and with implementations of two state of the art approaches. The results of our study indicate that the proposed approach outperforms both the original method and the two state of the art methods.